1. Field of the Invention
The present invention relates to information management and, more particularly, to a robust, web-enabled fully automated dynamic security price and share value comparator and indexer.
2. Description of the Background
The art of successful investing is based upon an ability to intelligently compare the values inherent in alternative investment opportunities. This comparison reflects the need for using the principle of opportunity cost. Opportunity cost is defined as the advantage forgone as the result of the acceptance of an alternative. It is measured both in prospect as well as retrospectively as the benefits that would result from the next best alternative investment that were rejected in favor of the one accepted. Opportunity cost is difficult, perhaps impossible, to measure precisely, and the concept is not used by investors in conducting any formalized investment analysis.
Traditional spread sheet analysis of security data is well known. There are a multitude of existing analysis techniques ranging from simple price earnings ratios to more complicated tracking models.
For example, U.S. Pat. No. 6,064,985 to Anderson shows an automated portfolio management system and method which manages data in a database, and populates the database with data from a data feed off the Internet. The system utilizes double leveraging of funds and purchased index puts as insurance against market downturns, to generate a guaranteed high yield. The double leveraging is accomplished by selling covered calls and using a formula to determine the maximum amount to borrow against the portfolio, and using income from both sources to purchase additional stock. The high yield from the portfolio is protected from market downturns by index puts.
U.S. Pat. No. 6,035,286 to Fried shows a computer implemented method for screening a database of stock information based upon buyback ratio and prices/sales ratio selection criteria. The buyback ratio represents the percentage of stocks repurchased by a company during a given period that resulted in a net decrease in outstanding shares. The method and system use the criteria to identify companies with a highest buyback ratio and with the lowest price/sale ratio. The resulting list of stocks are ranked and output in an investment report.
U.S. Pat. No. 6,003,018 to Michaud et al. discloses a method for evaluating a portfolio. Using the “Markowitz paradigm”, a portfolio may be optimized, with the goal of deriving the peak average return for a given level of risk and any specified set of constraints, in order to derive a so-called “mean-variance (MV) efficient” portfolio using known techniques of linear or quadratic programming as appropriate. A mean-variance efficient portfolio is computed for a plurality of simulations of input data statistically consistent with an expected return and expected standard deviation of return, and each such portfolio is associated, by means of an index, with a specified portfolio on the mean variance efficient frontier. A statistical mean of the index-associated mean-variance efficient portfolios is used for evaluating a portfolio for consistency with a specified risk objective.
U.S. Pat. No. 5,761,442 to Barr et al. shows a method for selecting securities based on a set of artificial neural networks which are designed to model and track the performance of each security in a given capital market and output a parameter which is related to the expected risk adjusted return for the security. Each artificial neural network is trained using a number of fundamental and price and volume history input parameters about the security and the underlying index. The system combines the expected return/appreciation potential data for each security via an optimization process to construct an investment portfolio which satisfies predetermined aggregate statistics. The data processing system receives input from the capital market and periodically evaluates the performance of the investment portfolio, rebalancing it whenever necessary to correct performance degradations.
Some of the foregoing tools and techniques have been made available on the internet, and at least a few incorporate executable Java scripts for accomplishing and graphing stock data. One known reference employs indexing. U.S. Pat. No. 6,061,663 to Bloom et al. (NASDAQ) shows a computer program product for rebalancing a capitalization weighted stock index in order to prevent the capitalization weight of a few high capitalized stocks from dominating the overall performance of the index. Index rebalancing is accomplished while maintaining the original relative position of stocks and reducing the market impact of rebalancing on the Small Individual Stock group.
While the foregoing reflects an adjustment to a stock index to account for large-capitalization companies, neither this nor any other existing technical analysis methods measure opportunity cost as reflected by the use of a pivot stock (as opposed to the calculation of pivot points). For instance, Bolverk's Lair by Arthur Corliss is a website that contains a Stock Pivot Tool that calculates pivot points using the traditional method, and produces historical graphs of both pivot points (with moving averages) and candlesticks. Price crossing the pivot on an up move is a bullish event; a cross below the pivot on a down move is a bearish event. Once a bullish indication has registered the study offers two resistance levels for the price to test. If the first resistance is penetrated price can be expected to test the second resistance. Professional investment decision making should be the result of a study of relative values as the absolute statistics only have relevance when related to alternatives. It would be greatly advantageous to facilitate the comparison in indexed terms, of a large number of user selected factors relating to a large number of user selected securities, all with regard to a single user-selectable “pivot” security. This would result in a better quality of decision making because the indexing would be based on the user's personal opportunity costs rather than a canned index (such as the Dow Jones). If used correctly, this approach could vastly improve the personal investment management performance of the user.